System and method for synthesizing data in a radar system

ABSTRACT

A system for synthesizing data received from a weather radar system includes a digital signal processor, a data synthesizer, a convolution filter, and data storage. The digital signal processor digitizes reflectivity data from a radar antenna. The data synthesizer generates an M×N matrix for a digitized reflectivity data point and populates each element in the M×N matrix with the value of the digitized reflectivity data point. The convolution filter calculates a filtered value for each element in the M×N matrix. The data storage stores the M×N matrix for display.

FIELD OF THE INVENTION

The present invention relates generally to the processing of data fromweather radar systems. More specifically, the present invention relatesto hardware and software to improve data resolution in weather radarsystems.

BACKGROUND OF THE INVENTION

The majority of weather radar systems utilize a signal processor todigitize an analog weather radar signal and then average a number ofsamples together to produce one ray of processed precipitationreflectivity. As processing evolved to digital signal processors thetechnique remained primarily the same. Digitizing sample clocks becamevariable to a degree. By adjusting the sample clocks, the rangeresolution of the processing system became more customizable. Pulsesample sizes also became more flexible. Adjustment of the pulse samplesizes made angular resolution variable.

These techniques allowed for sample volume coverage with controllablesample deviation. The limitations of this style of processing becamenoticeable as display systems became more capable of enlarging thesmaller weather areas which demonstrated the higher digitized equalityof the radar data as it was represented by a four-sided polygon. Inorder to better represent the weather data when magnified, a number ofdata algorithm techniques, and raster display algorithm techniques havebeen employed.

SUMMARY OF THE INVENTION

A system for synthesizing data received from a weather radar systemincludes a digital signal processor, a data synthesizer, a convolutionfilter, and data storage. The digital signal processor digitizesreflectivity data from a radar antenna. The data synthesizer generatesan M×N matrix for a digitized reflectivity data point and populates eachelement in the M×N matrix with the value of the digitized reflectivitydata point. The convolution filter calculates a filtered value for eachelement in the M×N matrix. The data storage stores the M×N matrix fordisplay.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram of a radar system;

FIG. 2 is a flow chart showing the steps of an embodiment of theinvention;

FIG. 3 is an example of geographically located radar data;

FIGS. 4A and 4B are examples of the steps of FIG. 2 in an embodiment ofthe present invention;

FIGS. 5A-5C are examples of another set of steps according to an aspectof the invention;

FIGS. 6A-6C are examples of further processing of the example of FIGS.5A-5C; and

FIGS. 7A-7C are examples of the steps in accordance with anotherembodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Turning now to the drawing figures, FIG. 1 is a system diagram of aradar system 10. The radar system 10 includes a digital signal processor12, a control module 14, data storage 16 and a GUI engine 18. The radarsystem 10 further includes a user interface 20, a display 22 and a radarantenna 28. A user inputs parameters through the user interface 20 tothe control module 14 in order to process the information from the radarantenna 28 for display on the display 22. The radar antenna 28 receivesa transmitted electromagnetic signal and concentrates that signal forprocessing. The electromagnetic signal has an amplitude and a phasewhich together denote characteristics of the atmosphere at a range, dand at an angular position, r. The orientation of the radar antenna 28at the time of collection of the transmitted electromagnetic signaldetermines the location of the atmospheric conditions denoted by thephase and amplitude of the electromagnetic signal. As the orientation ofthe radar antenna 28 changes, the electromagnetic signal received by theantenna denotes the atmospheric conditions of locations surrounding theantenna. The range of the detected atmospheric conditions is determinedby the time it takes for the radar to receive the electromagnetic signalfrom the time that signal was transmitted from the transmitter. Thus,the exact location of the atmospheric condition may be mapped from theradar antenna 28 location at a distance determined by the time forsignal reception and a direction determined by the rotated position ofthe radar antenna 28.

Generally, the radar antenna 28 is rotated on a pedestal. The antenna28, generally, includes both the transmitter and the receiver of thesignal. Such a configuration allows the antenna 28 to transmit andreceive the electromagnetic signal. As the antenna 28 rotates, theantenna 28 transmits and receives the signal in a 360° arc around theantenna position. As the antenna 28 receives the transmitted signal theantenna 28 transmits the signal to the digital signal processor 12 forprocessing. The digital signal processor 12 also records the directionof the antenna 28 and the time from transmission to reception of thesignal so that it may map the received signal to the two-dimensionalspace the distance, d from the antenna at the direction r from theantenna 28. The resolution of the antenna signal is determined by therotation rate of the antenna 28 for the direction r and is determined bythe pulse width of the transmitted signal for the distance d. Longerpulse widths and faster rotations produce less resolution in bothdirections. Because the resolution in the rotated direction r is anangular resolution, the further the distance d from the antenna the lessresolution of the received signal relative to the geographic region thedata represents.

The digital signal processor 12 is a special purpose central processingunit that provides fast instruction sequences, such as shift and add,and multiply and add which are commonly used in math intensive signalprocessing applications. Generally, the digital signal processor 12 maybe considered an embedded controller dedicated to the single task ofdigitizing the received analog signal from the antenna 28. The benefitof transforming the analog signal into a digital signal allows theindividual data points to be reduced to numbers, and thus analyzed inrelation to one another. Moreover, the digitized signals may be filteredto provided a more complete analysis of the local weather conditions.Digital signal processor 12 may perform the operations in real time.Moreover, filtering and data synthesis may also be performed in realtime.

The digital signal processor 12 includes a convolution filter 24 and adata synthesizer 26. The convolution filter 24, as described below,filters the digital signal to create a filtered digital signal. The datasynthesizer 26 synthesizes additional data points with greaterresolution than the received analog signals. Together, the datasynthesizer generates a higher resolution signal that the convolutionfilter 24 filters by analyzing the numerical value of the neighbors ofthe data point. The digital signal processor 12 outputs the digitizedfiltered data to the data storage 16 in the control unit 14.

The GUI engine 18 is a graphical user interface engine configured tocommunicate with the digital signal processor 12, the data storage 16and a user interface 20. The GUI engine 18 receives parameters from theuser interface 20 to set the filter resolution in the data synthesizer26 and convolution filter 24. A user may define the kernel size of theconvolution filter, the shape of the convolution filter, the weightsgiven to the neighbors of the elements being filtered, the size andshape of the data synthesized and the order of filtering the data. TheGUI engine 18 passes these parameters to the convolution filter 24 andthe data synthesizer 26 so that the digital signal processor 12 maycalculate the digital data stored in the data storage 16 according tothe desire of a user.

The display 22 and the user interface 20 are configured to interact withthe control unit 14 so that a user may input parameters through the userinterface 20 and review the output of those chosen parameters throughthe display 22. The user interface 20 may be as basic as a terminal withkeyboard and mouse entry, a touch-screen, or any other means forinputting data into a controller. The display 22 may generally be avideo screen to receive the video source from the data storage thatrepresents the digital information recorded by the digital signalprocessor 12.

Turning now to FIG. 2, FIG. 2 is a flow chart showing the steps of anembodiment of the invention. The method begins at step 40. Reflectivitydata is received in step 42, digitized in step 44, and temporarilystored in step 46. The method receives user parameters in step 48. Instep 50, range bin resolution is synthesized according to the parametersreceived in step 48. The angular bin resolution is synthesized in step52. The synthesized data is then input 54 into the convolution filter.Once the synthesized data is filtered, the method determines whethermore resolution is needed in step 56. If no further resolution isneeded, then the filtered data is stored in step 58. If more resolutionis needed, then the method determines whether the bin size should bechanged in step 60. If the bin size is not changed then the synthesizeddata is passed through the convolution filter in step 54 again. If thebin size is set to change, then the input data set parameter is reset instep 62 and the process returns at step 50 to synthesize the additionaldata set and then filter the signal again. When the resolution isadequate in step 56, and the data is stored in step 58, then the methodends in step 64.

In step 42, reflectivity data is received from the antenna. The data isan analog signal representing the reflected data from the transmittedsignals and includes both an amplitude and a phase component. Thereflectivity data contains information that can determine the size, typeand direction of hydrometeors by examining the phase, frequency, andamplitude of the received transmitted signal. In step 44, the data isdigitized from the analog signal of step 42. By examining thereflectivity data from step 42 and the transmitted signal, the in-phaseand quadrature signals may be generated. The in-phase and quadraturesignals determine the size, type and direction of the hydrometeors byexamining the phase shift, frequency shift and change in amplitude ofthe received signal compared to the transmitted signal. The data isgenerally displayed as an exponential number which is stored 46 intemporary memory.

Step 48 receives user parameters. A user may identify the size of theconvolution filter, the weighting of the convolution filter, the shapeof the convolution filter and any order of filtering. The user may alsodefine the size of the data synthesis for any single data point, orcell. As the user becomes more comfortable with the system and becomesmore familiar with the surrounding geography of the radar system, theuser will become more proficient at setting these variables for bestradar resolution. For example, if the majority of geographic locationsof interest to the user are close to the radar location, then the usermay wish to synthesize data only in the range direction and not in theangular direction. If, however, the location of interest will generallybe farther from the radar location the user may wish to synthesize datain both the angular direction and the range direction, or may wish tosynthesize more data in the angular direction relative to the rangedirection. A user may also wish to define the weight, size and shape ofthe convolution filter based upon the location and the amount ofsynthesis occurring in the method.

In step 50, the range bin resolution is synthesized. Synthesis firstrequires changing the exponential number to a floating point number. Thesynthesis also adds resolution to the data by resizing each data pointinto multiple data points. In step 50, the number of data points in thedirection of range is determined. In step 52, the number of data pointsin the angular direction is determined. The results of steps 50 and 52is a M×N matrix of M rows extending in the angular direction and Ncolumns extending in the range direction. Each of the cells in the M×Nmatrix is populated with value from the original 1×1 cell. For each datapoint in the stored data, an M×N matrix is generated to store each datapoint M×N times.

In step 54, the synthesized data from steps 50 and 52 are input into aconvolution filter. The convolution filter performs the mathematicalfiltering operation on each cell within each M×N matrix for each datapoint from the stored data. For each calculation of the filter, a newvalue for each element in each M×N matrix is calculated, but until allcells in all M×N matrices for all data points is calculated, theneighbor data for the calculation is based upon the original datapoints. Because the data represents data recovered from 360° around theradar antenna, the beginning of the file and the end of the filerepresent adjacent angular orientations of the local geographic area.Thus, there is no edge condition in the angular direction. In the rangedirection, an edge condition is generated at the outer most data pointsand a convolution filter must account for the edge in order to correctlycalculate the filtered values at the edge.

In step 56, the method determines whether more resolution is needed. Ifno more resolution is needed, then the M×N matrices for each data pointare stored in step 58. If more resolution is needed, then the methodrequests information from a user. If the user wishes to change the binsize in step 60, then the user inputs a data set representing the binsize for the range and angular resolution. The method then returns thoserange sizes to step 50. By inputting a new matrix size, M′×N′, theconvolution filter in step 54 will calculate M′×N′ data points for eachstored data point from step 46 for all data points in the angulardirection and range direction. If the user does not wish to change thebin resolution size of the matrices, then the resolution may beincreased by filtering the data from the already filtered data set. Thusthe data input into step 54 is the data having already been filtered onetime (i.e. a second order filter). If the output from step 54 againrequires more resolution, then either more bins may be added or higherorder filtering may be used. Alternatively, a combination of higherorder filtering and increased bin size may be used. Examples ofcombinations of higher order filtering and increased bin size are shownin FIGS. 4-7.

Turning now to drawing FIG. 3, FIG. 3 is an example of geographicallylocated radar data. The example includes row 66 and column 68representing the angular direction and range direction, respectively. Amatrix 70 is the synthesized data matrix for a single data pointrepresented by one cell in one row 66 and one column 68. The matrix 70is an M×N matrix, in this example, a 5×8 matrix. The matrix 70 thusprovides 5 times the resolution in the angular direction and 8 times theresolution in the range direction. Each element in the 5×8 matrix 70 ispopulated with the original data value from the original cell. Each cellin each row 66 and column 68 would similarly be populated by a matrix 70such that each cell would include 40 elements, each element having theinitial value of the original cell.

In this example, the radar location is to the left of the figure at aposition between the second and third rows of the figure. As the rangefrom the radar increases, the height of the rows 66 increases. The widthof the columns 68 remains constant because the width is determined bythe pulse width of the signal sent from the antenna. Because of thisconfiguration, the cells closest to the radar represent the smallestarea while the cells fartherest from the radar represent the greatestarea. In an alternate embodiment, the data synthesizer may vary thenumber of rows in the M×N matrix according to distance from the radar.In such an embodiment, the cells nearest the radar would have fewer rowswhile the cells farther from the radar would have more rows. In such anembodiment, each individual element in the matrix 70 may be sized suchthat any element in any matrix 70 would represent the same geographicsize. Such an embodiment may present a more uniform display.

Turning now to drawing FIGS. 4A and 4B, FIGS. 4A and 4B are examples ofthe steps of FIG. 2 in an embodiment of the present invention. FIG. 4Ashows a first order filter of a cell. The matrix 70 includes an M×Nmatrix having four rows and four columns, a square matrix. The matrix 70neighbors a left neighbor 72, an upper left neighbor 74, and upperneighbor 76, an upper right neighbor 78, a right neighbor 80, a lowerright neighbor 82, a lower neighbor 84, and a lower left neighbor 86.The original values for each of the neighbors is as follows:

TABLE 1 Cell Value Center 10 Left 8 Upper Left 2 Upper 5 Upper Right 8Right 10 Lower Right 9 Lower 7 Lower Left 6

The center cell, with a value of 10, populates each of the cells in thematrix 70. For example, cells 88A, 88B and 88C all initially start witha value of 10. In this example, the convolution filter is centerweighted, meaning the value for the matrix entry that the filter iscalculating is weighted equivalently to all surrounding cells. When thecenter cell is split into a 4×4 matrix, the neighbor cells 72, 76, 80and 84 (the left, upper, right and lower cells, respectively) are alsosplit into a 4×4 matrix. The corner neighbors 74, 78, 82 and 86 whichrepresent the upper left, upper right, lower right and lower leftneighbors have only a single cell which will neighbor the matrix 70. Thecalculation of any individual element in the matrix 70, such as 88A, 88Bor 88C is calculated using each of the individual elements immediateneighbors. For example, element 88A is calculated using the value of theelements 72A, 74, 76A, 76B, 88B, 72B and the elements represented at theintersection of column 76A and row 72B and column 76B and row 72B. Theinitial values for elements 88B, element at the intersection of column76A and row 72B and element at the intersection of column 76B and row72B all have an initial value of 10 because those are synthesized datapoints created in the original center cell having a value of 10. Thevalue of element 88A is calculated by adding the total value of each ofthe neighbors and 9 times the value of the element upon which the filteris centered. Thus, for element 88A, the calculation is2+5+5+10+10+10+8+8+9×10. This value is then averaged by dividing thesummed result by 17. The result for each element is shown in FIG. 4A inthe matrix 70.

FIG. 4B shows a second order filter based upon the results of the firstorder filter of FIG. 4A. In FIG. 4B, the neighbor elements 72-86 havemaintained the same initial value. However, the value of the elements inmatrix 70 are from the filter calculations of FIG. 4A. For example, theelement 88B is calculated using the value of the upper neighbor, thevalues in column 76C as intersects with rows 72A and 72B. The column 76Bintersects with column 72B and the column 76A elements intersected atrows 72A and 72B. Thus, the second order filter value for element 88 bin FIG. 4B is calculated as 5+5+5+9.12+10+10+9.65+8.71, and then dividedby the factor 17. By filtering the data a second time, the interiorelements of the matrix 70 are filtered having some componentscontributed to the value from the neighbor cells 72-86. However, theinterior elements of FIG. 4B are closer to the original value of thecenter cell (10) which is both expected and desired. The edge elementsof matrix 70 converge in value to the neighbor cells 72-86, whichprovides a graded slope from the values of the neighbor cells 72-86 tothe center cell matrix 70.

Turning now to drawing FIGS. 5A-5C, FIGS. 5A-5C are examples of anotherset of steps according to an aspect of the invention. FIG. 5A includes acenter cell 90 surrounded by the neighboring cells 72-86. The values forthe center cell 90 and the neighboring cells 72-86 are the same as inthe example of FIG. 4. The example of FIGS. 5A-5C shows data synthesisin a single direction, namely, the angular direction.

In FIG. 5B, the center cell 90 is split into two elements 90A and 90B.The left neighbor 72 is also split into two elements 72A and 72B and theright neighbor likewise is split into two elements. The initial value ofcells 90A and 90B, similar to the example of FIG. 4, is 10. To calculatethe value of element 90A, the neighbors are summed along with theinitial value of the cell weighted by a factor of 9. Thus, the value ofelement 90A is (2+5+8+10+10+10+8+8+9×10)/17. The value for element 90Bis (8+10+10+10+9+7+6+8+9×10)/17. These values are 8.88 and 9.29,respectively. As would be expected, element 90A is smaller given theneighbors to element 90A are relatively smaller (e.g., left upperneighbor value 2 and upper neighbor value 5). While element 90B has ahigher value due to the relatively high values of its neighbors (e.g.,lower neighbor value 7 and lower right neighbor value 9).

Turning now to FIG. 5C, the center cell 90 is both synthesized by afactor of 2 and filtered a second time. Center cell 90 is synthesized toinclude elements 90A, 90B, 90C and 90D. In order to calculate thesesynthesized elements, the left and right neighbors 72 and 80 are alsosplit into four elements as shown by elements 72A, 72B, 72C and 72D. Theinitial values for elements 90A and 90B are 8.88, which was the initialvalue for the element 90A of FIG. 5B. The initial value for elements 90Cand 90D is 9.29, which was the initial value for the bottom half of thecenter cell in FIG. 5B (element 90B). The value of the cells 90A-90Dare:

TABLE 2 Element Value 90A 8.22 90B 8.95 90C 9.17 90D 8.88

The values of Table 2 for the elements 90A-90D also show an expectedconvergence in value. The two interior elements 90B and 90C are closestto the original value of the cell while the outer elements 90A and 90Dtrack toward the values of the neighbor elements while heavily weighedtowards the initial value of the cell. In FIG. 5C, the angularresolution has increased four times the original resolution of FIG. 5Aand shows a gradient between the neighbors and the center cell 90. Thegradient allows a user to view the cell with additional information thatmay help the user to define the type, shape, and direction of theatmospheric event.

Turning now to drawing FIGS. 6A-6C, FIGS. 6A-6C are examples of furtherprocessing of the example of FIGS. 5A-5C. FIGS. 6A-6C show an example ofadding range resolution to the angularly resolved example of FIG. 5C.FIG. 6A is similar to the FIG. 5C. FIGS. 6B and 6C each double the rangeresolution by synthesizing data points in the range direction. In FIG.6B, the upper and lower neighbors 76 and 84 are split into two columns76A and 76B. Each element in the center cell along the same angularorientation is assigned the value from the element in the same angularorientation from FIG. 6A. Thus, element 90A in FIG. 6B is assigned aninitial value of 8.22. Similarly, the right neighbor of element 90A isalso assigned a value of 8.22. The lower right neighbor of element 90A(also the right neighbor of element 90B) is assigned a value of 8.95.Each element in the center cell is calculated similarly using theconvolution filter as previously discussed.

In FIG. 6C, the range resolution is amplified by a factor of 2 withrespect to FIG. 6B. Similar to the operation of FIG. 6B, each element inthe center cell is initially given a value based upon its angularposition and range position with respect to FIG. 6B. The calculationsfor any element in FIG. 6C, for example 90A, 90B or 90C, is based uponthe results of the calculations performed in FIG. 6B. Thus, theresolution of the center cell in FIG. 6C is both increased by a factorof 2 with respect to FIG. 6B and filtered for a second time from theoriginal angularly synthesized data set of FIG. 6A. The resolution ofFIG. 6C, as compared to the single data point in the center cell of FIG.5A, has been resolved by a factor of 16.

Turning now to drawing FIGS. 7A-7C, FIGS. 7A-7C are examples of thesteps in accordance with another embodiment of the present invention.FIG. 7A is the same, original example radar data for each of theprevious examples. In FIG. 7B, the center cell is synthesized in boththe angular direction and in the range direction by a factor of 2. Thecalculations for the 2×2 matrix of the center cell are calculated basedupon neighbors 72-86. The resulting filter data is shown in FIG. 7B. InFIG. 7C, the synthesized and filtered matrix of the center cell of FIG.7B is again synthesized by a factor of 2 in each of the angular andrange directions. Each element in the 4×4 center cell matrix is filteredaccording to the same function as previously described and the resultsare shown in FIG. 7C. The 4×4 matrix results for the center cell arestored and are used for display of the data.

While the data points of the examples of FIGS. 4-7 have been simplenumbers ranging from 2 to 10, actual data points received from the radarare generally not as simple. Instead, as previously described, the datapoints received from the radar antenna are in exponential form. Prior tosynthesizing and filtering the data, however, the exponential datapoints should be changed to floating point numbers so that the filteringdoes not skew the amplitudes of the synthesized data points because ofthe exponential function. After filtering is completed, the resultantsynthesized and filtered data should be returned to exponential formprior to display. In addition, the examples have shown a simplified formof the convolution filter which did not change the neighbors values72-86 in order to calculate the values of the elements in the centercell matrix. However, in application, because all data points would befiltered, each order filter would include values for the neighboringelements that had changed from the original value.

In alternate embodiments, the filter may take a shape other than asquare shape including only the nearest neighbors on each side. Forexample, a circular filter may be applied in which all neighboringelements within a certain circumference of the center cell may be usedto calculate the value of the center cell. In other embodiments, thefilter may include not only the neighbors but the neighbors of theneighbors and so on with weights for the neighbor cells assignedaccording to the distance from the center cell. Such weights may belinearly related according to distance from the center element, or maybe related in some other manner according to need or desire of a user.Furthermore, because the data points nearest to the radar occupy theleast space as previously discussed, it is possible to vary both thesynthesis process and the filtering process according to distance fromthe radar and according to the user defined parameters.

While the invention has been shown in embodiments described herein, itwill be obvious to those skilled in the art that the invention is not solimited but may be modified with various changes that are still withinthe spirit of the invention.

1. A system for synthesizing data received from a weather radar system,comprising: a. a digital signal processor configured to digitizereflectivity data from a radar antenna; b. said digital signal processorincluding a data synthesizer configured to generate an M×N matrix for adigitized reflectivity data point and further configured to populateeach element in said M×N matrix with the value of said digitizedreflectivity data point; c. said digital signal processor including aconvolution filter configured to calculate a filtered value for eachsaid element in said M×N matrix; and, d. data storage configured tostore said M×N matrix for display.
 2. The system of claim 1, whereinsaid data synthesizer is further configured to generate an M×N matrixfor each digitized reflectivity point.
 3. The system of claim 2, whereinsaid M×N matrix is a square matrix.
 4. The system of claim 1, furthercomprising a user interface configured to receive parameters for saiddata synthesizer and said convolution filter.
 5. The system of claim 4,wherein said parameters comprise the size of said M×N matrix.
 6. Thesystem of claim 5, wherein said user interface is configured to receivean update of said size of said M×N matrix, such that at least one of thevalues defining said size of said updated M×N matrix differs from saidM×N matrix.
 7. The system of claim 4, wherein said parameters comprisethe shape of said convolution filter.
 8. The system of claim 4, whereinsaid parameters comprise a weighting matrix for said convolution filter.9. The system of claim 4, wherein said parameters comprise an order forsaid convolution filter.
 10. The system of claim 1, further comprisingan inverse logarithmic base 10 operator configured to invertexponentiated digitized reflectivity data into floating point data. 11.The system of claim 10, further comprising a logarithmic base 10operator configured to exponentiate said filtered values for each saidelement in said M×N matrix.
 12. The system of claim 11, furthercomprising a display configured to display an intensity map of saidexponentiated filtered value for each said element in said M×N matrix.13. A method of synthesizing data received from a weather radar system,comprising: a. digitizing reflectivity data from a radar antenna; b.generating an M×N matrix for a digitized reflectivity data point; c.populating each element in said M×N matrix with the value of saiddigitized reflectivity data point; d. filtering each said element insaid M×N matrix; and e. storing said M×N matrix for display.
 14. Themethod of claim 13, wherein said generating step and said populatingstep generates and populates an M×N matrix for each digitizedreflectivity point.
 15. The method of claim 14, wherein said M×N matrixis a square matrix.
 16. The method of claim 13, further comprising thestep of synthesizing said M×N matrix into a larger M×N matrix after saidfiltering step.
 17. The method of claim 16, further comprising the stepof filtering said M×N matrix.
 18. The method of claim 13, furthercomprising the step of filtering again said M×N matrix after saidfiltering step.
 19. The method of claim 13, wherein said filtering stepuses a convolution filter to filter said M×N matrix.
 20. The method ofclaim 13, further comprising the step of inverting exponential digitizedreflectivity data into floating point data.
 21. The method of claim 20,further comprising the step of exponentiating said filtered M×N matrix.22. The method of claim 21, further comprising displaying an intensitymap of said exponentiated and filtered M×N matrix.
 23. A system forsynthesizing data received from a weather radar system, comprising: a.means for digitizing reflectivity data from a radar antenna; b. meansfor generating an M×N matrix for a digitized reflectivity data pointbased upon said digitized reflectivity data; c. means for populatingeach element in said M×N matrix with the value of said digitizedreflectivity data point; d. means for filtering each said element insaid M×N matrix; and e. means for storing said M×N matrix for display.24. The system of claim 23, wherein said means for generating is furtherconfigured to generate an M×N matrix for each digitized reflectivitypoint.
 25. The system of claim 24, wherein said M×N matrix is a squarematrix.
 26. The system of claim 23, further comprising means forreceiving parameters for said means for generating and said means forfiltering.
 27. The system of claim 26, wherein said parameters comprisethe size of said M×N matrix.
 28. The system of claim 27, wherein saidmeans for receiving parameters is configured to receive an update ofsaid size of said M×N matrix, such that at least one of the valuesdefining said size of said updated M×N matrix differs from said M×Nmatrix.
 29. The system of claim 26, wherein said parameters comprise theshape of a convolution filter.
 30. The system of claim 26, wherein saidparameters comprise a weighting matrix for said convolution filter. 31.The system of claim 26, wherein said parameters comprise an order forsaid convolution filter.
 32. The system of claim 23, further comprisingmeans for inverting exponential digitized reflectivity data intofloating point data.
 33. The system of claim 32, further comprisingmeans for exponentiating said filtered values for each said element insaid M×N matrix.
 34. The system of claim 33, further comprising meansfor displaying an intensity map of said exponentiated filtered value foreach said element in said M×N matrix.